Diagnosis of abnormal cardiac conditions has relied in the past on visible alterations in the P, QRS, and T-waves, i.e. portions of the electrocardiograph periodic signal. The electrocardiograph signal includes a low frequency portion and an impressed or imbedded high frequency portion, and it has been found that although the higher frequency portion of the signal is not particularly visible, it contains information that provides greater sensitivity in determining certain abnormalities, notably myocardial ischemia and infarction.
The conventional electrocardiogram (ECG) can be a very insensitive diagnostic tool. For example, a significant percentage of individuals presenting to a hospital emergency room with an actual myocardial infarction (heart attack) will have a normal 12-lead conventional ECG. In addition, the conventional ECG accurately reflects only the predominant low-frequency electrical activity of the heart. It tells the clinician little or nothing about the less predominant high frequency components of the heart's electrical signal embedded within the various lower-frequency waves of the conventional ECG.
From off-line studies, it is known that a diminution of the higher frequency components within the central portion of the QRS complex of the ECG can be a highly sensitive indicator for the presence of myocardial ischemia or infarction, more sensitive, for example, than changes in the ST segment of the conventional low-frequency electrocardiogram. However, until now, there has been no device capable of displaying, in real time, changes in these high frequency QRS components in the monitored patient. While academic software programs have been designed that analyze the central high frequency QRS components, all such programs involve laborious off-line calculations and post-processing, and therefore have little if any clinical utility, being strictly research tools. Thus, there remains a need for a system and method that analyzes high frequency components over the entire QRS interval in real time for usefulness in the clinical environment. Such a system should perform, in real time, all of the complex digital sampling, averaging, and filtering that is required to generate high frequency QRS ECG signals. The system should also thereafter update these high frequency QRS ECG signals, as well as other derived parameters, in real time on a beat-to-beat basis, supplementing the diagnostic information being obtained from the conventional (i.e. low frequency) ECG complexes at the same time.
The higher frequency signals in the central portion of the QRS ECG complex that have generated the most research interest in terms of off-line detection of ischemia and infarction are those signals in the range of 150 to 250 Hz. The raw, analog ECG signal is typically sampled at ≧500 samples per second (to digitize the signal) in order to adequately satisfy the Nyquist rate of sampling at least twice the highest frequency of interest and in order to retain the information in the signal without loss. In the past, the sampled data have been stored, and then later processed to provide potentially useful information to the researcher.
On the other hand, Simpson, in U.S. Pat. No. 4,422,459, teaches a system which analyzes only the late portion of the QRS interval and early portion of the ST segment, and in an off-line fashion (i.e. from previously stored data) to indicate cardiac abnormalities, in particular the propensity for cardiac arrhythmia. The late portion of a post myocardial infarct patient's QRS waveform contains a high frequency (40-250 Hz) signal tail which is indicative of a tendency toward ventricular tachycardia. The system in Simpson digitally processes and filters a patient's QRS signals in a reverse time manner to isolate the high frequency tail and avoid the filter ringing which would otherwise hide the signal. Thus, in order to do so, Simpson presupposes that the data are stored so that they can be processed in reverse time order.
Albert et al. U.S. Pat. No. 5,117,833, partially focuses on analyzing signals within the mid-portion of the QRS interval for the indication of cardiac abnormality. The system of Albert et al. uses a known technique of building up data points to derive an average of heartbeat characteristics in order to enhance signal to noise ratio. Data are collected and filtered and then stored for subsequent analysis. Thus, the system does not teach a cardiac monitor which provides the data analysis immediately from the data derived from a patient, i.e. in “real-time”.
Albert et al., U.S. Pat. No. 5,046,504, similarly teaches the acquisition of QRS data and subsequent analysis. Routine calculations are performed from the data previously calculated and stored. Further, this system teaches producing a set of digital spectrum values representative of an approximate power density spectrum at each of a large number of generally equally spaced sampling time intervals of the ECG waveform.
Seegobin, in U.S. Pat. Nos. 5,655,540 and 5,954,664, provides a method for identifying coronary artery disease. The method relies on a database of high and low frequency ECG data taken from known healthy and diseased subjects. Comparison of the data has led to a “Score” component, indicating deviation of a patient's data from the norm. This reference is rather calculation intensive, and does not suggest monitoring the condition of a patient, but rather is utilized as an off-line diagnostic tool.
Hutson, U.S. Pat. No. 5,348,020, teaches a technique of near real-time analysis and display. The technique includes inputting ECG data from multiple, sequential time intervals and formatting those data into a two-dimensional matrix. The matrix is then decomposed to obtain corresponding singular values and vectors for data compression. The compressed form of the matrix is analyzed and filtered to identify and enhance ECG signal components of interest. As with other systems, this reference focuses on late potentials, a fraction of the QRS interval, as the tool to identify cardiac disease.
Finally, High-Frequency Electrocardiogram Analysis of the Entire QRS in the Diagnosis and Assessment of Coronary Artery Disease by Abboud (Progress in Cardiovascular Diseases, Vol. XXXV, No. 5 (March/April), 1993: pp 311-328) teaches the concept of “reduced amplitude zone” (RAZ) as a diagnostic tool. However, this reference also uses post-processing, and provides no teaching of a real-time analysis system.
Thus, there remains a need for an electrocardiograph that analyzes, in real time, the high frequency components of the QRS complex in order to provide an effective monitor for patients with specific cardiac function abnormalities. The present invention is directed to such an electrocardiograph.